Open-source release of Unsupervised Adversarial Training (UAT)

PiperOrigin-RevId: 267603150

unsupervised_adversarial_training/save_example_images.py

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+# coding=utf-8
+# Copyright 2019 Deepmind Technologies Limited.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+r"""Loads images from the 80M@200K training set and saves them in PNG format.
+
+Usage:
+    cd /path/to/deepmind_research
+    python -m unsupervised_adversarial_training.save_example_images \
+        --data_bin_path=/path/to/tiny_images.bin
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+from absl import app
+from absl import flags
+import numpy as np
+from PIL import Image
+
+DIR_NAME = os.path.dirname(__file__)
+FLAGS = flags.FLAGS
+flags.DEFINE_string('data_bin_path', None,
+                    'path to 80M Tiny Images data binary')
+flags.DEFINE_string('idxs_path', os.path.join(DIR_NAME, 'tiny_200K_idxs.txt'),
+                    'path to file of indices indicating subset of 80M dataset')
+flags.DEFINE_string('output_dir', os.path.join(DIR_NAME, 'images'),
+                    'path to output directory for images')
+flags.mark_flag_as_required('data_bin_path')
+
+CIFAR_LABEL_IDX_TO_NAME = ['airplane', 'automobile', 'bird', 'cat', 'deer',
+                           'dog', 'frog', 'horse', 'ship', 'truck']
+DATASET_SIZE = 79302017
+
+
+def _load_dataset_as_array(ds_path):
+  dataset = np.memmap(filename=ds_path, dtype=np.uint8, mode='r',
+                      shape=(DATASET_SIZE, 3, 32, 32))
+  return dataset.transpose([0, 3, 2, 1])
+
+
+def main(unused_argv):
+  dataset = _load_dataset_as_array(FLAGS.data_bin_path)
+
+  # Load the indices and labels of the 80M@200K training set
+  data_idxs, data_labels = np.loadtxt(
+      FLAGS.idxs_path,
+      delimiter=',',
+      dtype=[('index', np.uint64), ('label', np.uint8)],
+      unpack=True)
+
+  # Save images as PNG files
+  if not os.path.exists(FLAGS.output_dir):
+    os.makedirs(FLAGS.output_dir)
+  for i in range(100):
+    class_name = CIFAR_LABEL_IDX_TO_NAME[data_labels[i]]
+    file_name = 'im{}_{}.png'.format(i, class_name)
+    file_path = os.path.join(FLAGS.output_dir, file_name)
+    img = dataset[data_idxs[i]]
+    Image.fromarray(img).save(file_path)
+
+
+if __name__ == '__main__':
+  app.run(main)